{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DMJ6JMN5B3B3T3P2KI5WU6P2E2","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f135148007a59f22640f4e2f0fa30707ca15a64ed2f6b88c34e20fdcc1750493","cross_cats_sorted":["cs.AI","cs.MM","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2025-05-16T05:42:25Z","title_canon_sha256":"d5945f6c98c8cd83c81e83a33c0245e79e26396941cafa6927cc99e8c4ff0074"},"schema_version":"1.0","source":{"id":"2505.10885","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.10885","created_at":"2026-06-23T00:11:47Z"},{"alias_kind":"arxiv_version","alias_value":"2505.10885v1","created_at":"2026-06-23T00:11:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10885","created_at":"2026-06-23T00:11:47Z"},{"alias_kind":"pith_short_12","alias_value":"DMJ6JMN5B3B3","created_at":"2026-06-23T00:11:47Z"},{"alias_kind":"pith_short_16","alias_value":"DMJ6JMN5B3B3T3P2","created_at":"2026-06-23T00:11:47Z"},{"alias_kind":"pith_short_8","alias_value":"DMJ6JMN5","created_at":"2026-06-23T00:11:47Z"}],"graph_snapshots":[{"event_id":"sha256:a70e5609ac34e303968fa01d31c01eb00a0592cdbecb9c2af82171d36094950c","target":"graph","created_at":"2026-06-23T00:11:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.10885/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deepfake audio detection is challenging for low-resource languages like Bengali due to limited datasets and subtle acoustic features. To address this, we introduce BangalFake, a Bengali Deepfake Audio Dataset with 12,260 real and 13,260 deepfake utterances. Synthetic speech is generated using SOTA Text-to-Speech (TTS) models, ensuring high naturalness and quality. We evaluate the dataset through both qualitative and quantitative analyses. Mean Opinion Score (MOS) from 30 native speakers shows Robust-MOS of 3.40 (naturalness) and 4.01 (intelligibility). t-SNE visualization of MFCCs highlights r","authors_text":"Istiaq Ahmed Fahad, Kamruzzaman Asif, Sifat Sikder","cross_cats":["cs.AI","cs.MM","eess.AS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2025-05-16T05:42:25Z","title":"BanglaFake: Constructing and Evaluating a Specialized Bengali Deepfake Audio Dataset"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10885","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d4f4105ea8fa6b1a39df15616e51d9c4550ea17fa24ed3b77feef1a1886adc45","target":"record","created_at":"2026-06-23T00:11:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f135148007a59f22640f4e2f0fa30707ca15a64ed2f6b88c34e20fdcc1750493","cross_cats_sorted":["cs.AI","cs.MM","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2025-05-16T05:42:25Z","title_canon_sha256":"d5945f6c98c8cd83c81e83a33c0245e79e26396941cafa6927cc99e8c4ff0074"},"schema_version":"1.0","source":{"id":"2505.10885","kind":"arxiv","version":1}},"canonical_sha256":"1b13e4b1bd0ec3b9edfa523b6a79fa268d4778d48d44a5e49d1b40ac9c65ff53","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1b13e4b1bd0ec3b9edfa523b6a79fa268d4778d48d44a5e49d1b40ac9c65ff53","first_computed_at":"2026-06-23T00:11:47.738415Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-23T00:11:47.738415Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"H7kwzd+QDOpllUYEAJB/vkbnGVwc5PnNQSQsfQY5jLEQxWa8Y9OdstHoqeJ83f9qyGwChNRzCeMHiyaAGfhKBQ==","signature_status":"signed_v1","signed_at":"2026-06-23T00:11:47.738876Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.10885","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d4f4105ea8fa6b1a39df15616e51d9c4550ea17fa24ed3b77feef1a1886adc45","sha256:a70e5609ac34e303968fa01d31c01eb00a0592cdbecb9c2af82171d36094950c"],"state_sha256":"b1f76c5dd5fb075090165eb018a61166185a5dd9601ccd57ed2b4a4d43e4401f"}